Research progress in Internet of vehicles trajectory privacy protection

doi:10.11772/j.issn.1001-9081.2017.07.1921

Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (7): 1921-1925.DOI: 10.11772/j.issn.1001-9081.2017.07.1921

Previous Articles Next Articles

Research progress in Internet of vehicles trajectory privacy protection

ZHANG Chunhua¹, ZANG Haijuan², XUE Xiaoping¹, ZHANG Fang¹, CHEN Kangqiang¹, FENG Lijuan¹

1. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;
2. College of Computer Engineering, Jiangsu University of Technology, Changzhou Jiangsu 213001, China

Received:2017-01-24 Revised:2017-03-23 Online:2017-07-10 Published:2017-07-18
Supported by:
This work is partially supported by the National Key Technology R&D Program during the Twelfth Five-year Plan Period (2015BAG13B01), the Prospective Joint Research Project of Jiangsu Province (BY2014038-03).

车联网轨迹隐私保护研究进展

张春花¹, 臧海娟², 薛小平¹, 张芳¹, 陈康强¹, 冯丽娟¹

1. 同济大学电子与信息工程学院, 上海 201804;
2. 江苏理工学院计算机工程学院, 江苏常州 213001

通讯作者: 臧海娟
作者简介:张春花(1989-),女,山东莒县人,博士研究生,主要研究方向:信息安全、隐私保护;臧海娟(1965-),女,江苏常州人,副教授,博士,主要研究方向:网络与信息安全;薛小平(1963-),男,上海人,教授,主要研究方向:可信计算、信息安全;张芳(1971-),女,上海人,讲师,博士,主要研究方向:可信计算;陈康强(1992-),男,浙江金华人,硕士研究生,主要研究方向:VANET隐私与安全;冯丽娟(1994-),女,四川雅安人,硕士研究生,主要研究方向:VANET安全与隐私。
基金资助:
“十二五”国家科技支撑计划项目（2015BAG13B01）；江苏省前瞻性联合研究项目（BY2014038-03）。

Abstract

Abstract: Trajectory privacy protection is critical to the development of Internet of Vehicles (IoV), which makes it important to summarize and analyze existing research methods. Existing IoV trajectory privacy protection methods can be divided into three categories: trajectory obfuscation, pseudonym change and trajectory encryption. Trajectory obfuscation can be achieved based on users' real trajectory or dummy trajectory. Pseudonym change can be achieved based on mix-zone or path confusion. Trajectory encryption can be achieved based on Private Information Retrieval (PIR) protocol or spatial transformation. Firstly, the research background and common attacks were introduced and summarized in this paper. Secondly, existing IoV trajectory privacy protection methods were surveyed from the aspects of methodology, scientific problem and method evolution. The problems need to be further studied also were elaborated. Furthermore, the performances of representative schemes were summarized, such as privacy protection, attack resistance and complexity. Finally, the future research directions of IoV trajectory privacy protection was prospected.

Key words: Internet of Vehicles (IoV), trajectory privacy, privacy protection

摘要： 轨迹隐私保护对车联网（IoV）的发展至关重要，归纳和分析现有研究方法有重要意义。车联网轨迹隐私保护思想有轨迹模糊、假名更换和轨迹加密等3类，实现方法分别有基于用户真实轨迹的方法和基于哑元轨迹的方法、基于混合区域的方法和基于路径混淆的方法、基于私密信息检索（PIR）协议的方法和基于空间转换的方法。首先，介绍和归纳了研究背景和常见攻击等车联网轨迹隐私保护关键问题；然后，从方法思想、科学问题、方法演进等方面详细综述了现有车联网轨迹隐私保护方法，并阐述了需深入研究的难题；在此基础上，总结了代表性方案的隐私保护度、抗攻击性、复杂度等性能指标；最后展望了车联网轨迹隐私保护的未来研究方向。

关键词: 车联网, 轨迹隐私, 隐私保护

CLC Number:

ZHANG Chunhua, ZANG Haijuan, XUE Xiaoping, ZHANG Fang, CHEN Kangqiang, FENG Lijuan. Research progress in Internet of vehicles trajectory privacy protection[J]. Journal of Computer Applications, 2017, 37(7): 1921-1925.

张春花, 臧海娟, 薛小平, 张芳, 陈康强, 冯丽娟. 车联网轨迹隐私保护研究进展[J]. 计算机应用, 2017, 37(7): 1921-1925.

References

[1] 陈宇峰,向郑涛,董亚波,等.车联网建模和统计性质分析及其路由策略综述[J].计算机应用,2015,35(12):3321-3324.(CHEN Y F, XIANG Z T, DONG Y B, et al. Review of modeling, statistical properties analysis and routing strategies optimization in Internet of vehicles[J]. Journal of Computer Applications, 2015, 35(12):3321-3324.)
[2] LU R. Security and privacy preservation in vehicular social networks[D]. Waterloo:University of Waterloo, 2012:18-28.
[3] WANG Y, XIA Y, HOU J, et al. A fast privacy-preserving framework for continuous location-based queries in road networks[J]. Journal of Network and Computer Applications, 2015, 53(7):57-73.
[4] SONG D, PARK K. A privacy-preserving location-based system for continuous spatial queries[J/OL]. Mobile Information Systems, 2016, 2016:1-9[2016-09-25]. http://dx.doi.org/10.1155/2016/6182769.
[5] KIDO H, YANAGISAWA Y, SATOH T. An anonymous communication technique using dummies for location-based services[C]//Proceeding of the 21st International Conference on Data Engineering Workshops. Piscataway, NJ:IEEE, 2005:88-97.
[6] TRAN M T, ECHIZEN I, DUONG A D. Binomial-mix-based location anonymizer system with global dummy generation to preserve user location privacy in location-based services[C]//Proceedings of the 2010 International Conference on Availability, Reliability, and Security. Piscataway, NJ:IEEE, 2010:580-585.
[7] HARA T, SUZUKI A, IWATA M, et al. Dummy-based user location anonymization under real-world constraints[J]. IEEE Access, 2016, 4:673-687.
[8] LU R, LIN X, LUAN T H, et al. Pseudonym changing at social spots:an effective strategy for location privacy in VANETs[J]. IEEE Transactions on Vehicular Technology, 2012, 61(1):86-96.
[9] YING B, MAKRAKIS D. Reputation-based pseudonym change for location privacy in vehicular networks[C]//Proceedings of the 2015 IEEE International Conference on Communications. Piscataway, NJ:IEEE, 2015:7041-7046.
[10] YU R, KANG J, HUANG X, et al. MixGroup:accumulative pseudonym exchanging for location privacy preservation in vehicular social networks[J]. IEEE Transactions on Dependable and Secure Computing, 2016, 13(1):93-105.
[11] HOH B, GRUTESER M. Protecting location privacy through path confusion[C]//Proceedings of the First International Conference on Security and Privacy for Emerging Areas in Communications Networks. Piscataway, NJ:IEEE, 2005:194-205.
[12] MEYEROWITZ J, ROY CHOUDHURY R. Hiding stars with fireworks:location privacy through camouflage[C]//Proceedings of the 15th Annual International Conference on Mobile Computing and Networking. New York:ACM, 2009:345-356.
[13] MOURATIDIS K, YIU M L. Shortest path computation with no information leakage[J]. Proceedings of the VLDB Endowment, 2012, 5(8):692-703.
[14] WANG L, MA R, MENG X. Evaluating k nearest neighbor query on road networks with no information leakage[C]//WISE 2015:Proceedings of the 16th International Conference on Web Information Systems Engineering. Berlin:Springer, 2015:508-521.
[15] KHOSHGOZARAN A, SHIRANI-MEHR H, SHAHABI C. Blind evaluation of location based queries using space transformation to preserve location privacy[J]. GeoInformatica, 2013, 17(4):599-634.
[16] 田丰,桂小林,张学军,等.基于兴趣点分布的外包空间数据隐私保护方法[J].计算机学报,2014,37(1):123-138.(TIAN F, GUI X L, ZHANG X J, et al. Privacy-preserving approach for outsourced spatial data based on POI distribution[J]. Chinese Journal of Computers, 2014, 37(1):123-138.)
[17] 周长利,马春光,杨松涛.路网环境下保护LBS位置隐私的连续KNN查询方法[J].计算机研究与发展,2015,52(11):2628-2644.(ZHOU C L, MA C G, YANG S T. Location privacy-preserving method for LBS continuous KNN query in road networks[J]. Journal of Computer Research and Development, 2015, 52(11):2628-2644.)
[18] PENG T, LIU Q, WANG G. Enhanced location privacy preserving scheme in location-based services[J]. IEEE Systems Journal, 2014, 11(1):219-230.
[19] KATO R, IWATA M, HARA T, et al. User location anonymization method for wide distribution of dummies[C]//Proceedings of the 2013 International Conference on Database and Expert Systems Applications. Berlin:Springer, 2013:259-273.
[20] ATAEI M, KRAY C. Ephemerality is the new black:a novel perspective on location data management and location privacy in LBS[M]//Progress in Location-Based Services 2016. Berlin:Springer, 2017:357-373.
[21] SONG J H, WONG V W S, LEUNG V C M. Wireless location privacy protection in vehicular Ad-Hoc networks[J]. Mobile Networks and Applications, 2010, 15(1):160-171.
[22] HUANG L, MATSUURA K, YAMANE H, et al. Enhancing wireless location privacy using silent period[C]//Proceedings of the 2005 Wireless Communications and Networking Conference. Piscataway, NJ:IEEE, 2005:1187-1192.
[23] SAMPIGETHAYA K, LI M, HUANG L, et al. AMOEBA:robust location privacy scheme for VANET[J]. IEEE Journal on Selected Areas in Communications, 2007, 25(8):1569-1589.
[24] BUTTYÁN L, HOLCZER T, WEIMERSKIRCH A, et al. SLOW:a practical pseudonym changing scheme for location privacy in VANETs[C]//Proceedings of the 2009 Vehicular Networking Conference. Piscataway, NJ:IEEE, 2009:1-8.
[25] SCHEUER F, FUCHS K P, FEDERRATH H. A safety-preserving mix zone for VANETs[M]//Trust, Privacy and Security in Digital Business. Berlin:Springer, 2011:37-48.
[26] PALANISAMY B, LIU L. MobiMix:protecting location privacy with mix-zones over road networks[C]//Proceedings of the 2011 International Conference on Data Engineering. Piscataway, NJ:IEEE, 2011:494-505.
[27] LIU X, ZHAO H, PAN M, et al. Traffic-aware multiple mix zone placement for protecting location privacy[C]//Proceedings of the 31st Annual IEEE International Conference on Computer Communications. Piscataway, NJ:IEEE, 2012:972-980.
[28] PALANISAMY B, LIU L, LEE K, et al. Anonymizing continuous queries with delay-tolerant mix-zones over road networks[J]. Distributed and Parallel Databases, 2014, 32(1):91-118.
[29] CHOR B, KUSHILEVITZ E, GOLDREICH O, et al. Private information retrieval[J]. Journal of the ACM, 1998, 45(6):965-981.
[30] WU D J, ZIMMERMAN J, PLANUL J, et al. Privacy-preserving shortest path computation[EB/OL]. (2016-01-10)[2017-01-25]. http://arxiv.org/abs/1601.02281.
[31] GHINITA G, KALNIS P, KHOSHGOZARAN A, et al. Private queries in location based services:anonymizers are not necessary[C]//Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. New York:ACM, 2008:121-132.
[32] PAPADOPOULOS S, BAKIRAS S, PAPADIAS D. Nearest neighbor search with strong location privacy[J]. Proceedings of the VLDB Endowment, 2010, 3(1/2):619-629.
[33] KHOSHGOZARAN A, SHAHABI C. Blind evaluation of nearest neighbor queries using space transformation to preserve location privacy[C]//Proceedings of the 2007 International Symposium on Spatial and Temporal Databases. Berlin:Springer, 2007:239-257.

Research progress in Internet of vehicles trajectory privacy protection

车联网轨迹隐私保护研究进展

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	LI Zhuo, SONG Zihui, SHEN Xin, CHEN Xin. Local differential privacy protection mechanism for mobile crowd sensing with edge computing [J]. Journal of Computer Applications, 2021, 41(9): 2678-2686.
[2]	CHEN Weiwei, CAO Li, GU Xiang. E-forensics model for internet of vehicles based on blockchain [J]. Journal of Computer Applications, 2021, 41(7): 1989-1995.
[3]	WANG Jiarui, TAN Guoping, ZHOU Siyuan. Clustered wireless federated learning algorithm in high-speed internet of vehicles scenes [J]. Journal of Computer Applications, 2021, 41(6): 1546-1550.
[4]	QIN Jing, AN Wen, JI Changqing, WANG Zumin. Review of privacy protection mechanisms in wireless body area network [J]. Journal of Computer Applications, 2021, 41(4): 970-975.
[5]	LIU Xiangyu, XIA Guoping, XIA Xiufeng, ZONG Chuanyu, ZHU Rui, LI Jiajia. Personalized privacy protection for spatio-temporal data [J]. Journal of Computer Applications, 2021, 41(3): 643-650.
[6]	WU Guangfu, WANG Yingjun. Secure storage and sharing scheme of internet of vehicles data based on hybrid architecture of blockchain and cloud-edge computing [J]. Journal of Computer Applications, 2021, 41(10): 2885-2892.
[7]	CHEN Jinyu, LIU Zhaowei. Internet of vehicles system based on improved proof of vote consensus protocol [J]. Journal of Computer Applications, 2021, 41(1): 170-176.
[8]	LYU Jiayu, ZHU Zhirong, YAO Zhiqiang. Two-channel dynamic data encryption strategy in cloud computing environment [J]. Journal of Computer Applications, 2020, 40(8): 2268-2273.
[9]	YANG Yang, WANG Ruchuan. Location based service location privacy protection method based on location security in augmented reality [J]. Journal of Computer Applications, 2020, 40(5): 1364-1368.
[10]	BAI Yameng, MAN Junfeng, ZHANG Hong. Secure storage model of electronic health records based on blockchain [J]. Journal of Computer Applications, 2020, 40(4): 961-965.
[11]	HUANG Chen, CAO Jiannong, WANG Shihui, ZHANG Yan. Dynamic reinforcement model for driving safety based on cooperative feedback control in Internet of vehicles [J]. Journal of Computer Applications, 2020, 40(4): 1209-1214.
[12]	LUO Huiwen, LONG Shigong. Location perturbation algorithm based on geo-indistinguishability of user’s region of interest [J]. Journal of Computer Applications, 2020, 40(3): 760-764.
[13]	LIU Xiangyu, CHEN Jinmei, XIA Xiufeng, Manish Singh, ZONG Chuanyu, ZHU Rui. Preventing location disclosure attacks through generating dummy trajectories [J]. Journal of Computer Applications, 2020, 40(2): 479-485.
[14]	ZHANG Meishu, XU Yabin. Personalized privacy protection method for data with multiple numerical sensitive attributes [J]. Journal of Computer Applications, 2020, 40(2): 491-496.
[15]	CHEN Weiwei, CAO Li, SHAO Changhong. Blockchain based efficient anonymous authentication scheme for IOV [J]. Journal of Computer Applications, 2020, 40(10): 2992-2999.